Modeling cocaine traffickers and counterdrug interdiction forces as a complex adaptive system

Area of Science:

• Criminology
• Computational Social Science
• Drug Policy Analysis

Background:

• US counterdrug interdiction aims to disrupt cocaine flow from South America to the US.
• Despite decades of effort, cocaine trafficking through Central America continues to increase.
• Existing strategies focus on supply-side disruption, but trafficking routes persist and evolve.

Purpose of the Study:

• To investigate the root causes of counterdrug interdiction ineffectiveness.
• To model narco-trafficker decision-making in response to interdiction.
• To understand how interdiction itself may contribute to trafficking resilience and spatial spread.

Main Methods:

• Development of a spatially explicit agent-based model (ABM) named "NarcoLogic".
• Model incorporates theoretical perspectives, empirical studies, media reports, and field research.
• Parameterization and validation using data on illicit cocaine flows.

Main Results:

• The model successfully reproduced the dynamic "cat-and-mouse" relationship between traffickers and interdiction forces.
• Simulations generated realistic spatial and temporal patterns of cocaine trafficking.
• Findings suggest trafficking proliferation is a natural consequence of interdiction, not solely due to its ineffectiveness.

Conclusions:

• NarcoLogic provides an evidence-based tool for assessing drug policy scenarios.
• The model can predict impacts on trafficker behavior and collateral damage.
• Results highlight the complex, adaptive nature of drug trafficking in response to enforcement pressure.